Constraints on adaptation: explaining deviation from optimal sex ratio using artificial neural networks

Diversity loss is predicted to increase extinction risk of specialist animals by constraining their ability to expand niche

Specialist animals are at a greater risk of extinction in the face of environmental change than generalist ones. The inability of some specialist taxa to expand host range through evolution may exacerbate or cause their high extinction risk. Here we use connectionism (a framework for modelling animal behaviour) to predict the environmental and physiological factors that predispose some specialist taxa to an 'evolutionary dead-end'. Neural networks are evolved to become resource-specialised in a resource-abundant and resource-diverse 'historical' environment while losing 'genes' that should restrict their ability to expand their host range. Networks are subsequently challenged to escape their dead-end by expanding host range in a 'contemporary' environment that may have depleted resource abundance and diversity (as many human impacted environments do). Loss of diversity in available resources universally constrains the ability of networks to expand host range and this effect is very robust to network conformation. Environmental resource abundance is more variable in its effect. Networks are generally robust to loss of genetic diversity during the evolution of specialisation except at very high rates of loss. By omitting historical specialisation, we show that the effect of resource diversity on host range expansion is not a universal network property but something that is often specific to specialist organisms. Historical specialisation also slightly reduces the robustness of networks in the contemporary environment to loss of genetic diversity during the specialisation process. Fundamentally, simulations predict that loss of local resource diversity will further increase the vulnerability of specialists to extinction by constraining their ability to expand host range in the face of environmental change.

Sex allocation in a polyembryonic parasitoid with female soldiers: an evolutionary simulation and an experimental test

Parasitoid wasps are convenient subjects for testing sex allocation theory. However, their intricate life histories are often insufficiently captured in simple analytical models. In the polyembryonic wasp Copidosoma koehleri, a clone of genetically identical offspring develops from each egg. Male clones contain fewer individuals than female clones. Some female larvae develop into soldiers that kill within-host competitors, while males do not form soldiers. These features complicate the prediction of Copidosoma's sex allocation. We developed an individual-based simulation model, where numerous random starting strategies compete and recombine until a single stable sex allocation evolves. Life-history parameter values (e.g., fecundity, clone-sizes, larval survival) are estimated from experimental data. The model predicts a male-biased sex allocation, which becomes more extreme as the probability of superparasitism (hosts parasitized more than once) increases. To test this prediction, we reared adult parasitoids at either low or high density, mated them, and presented them with unlimited hosts. As predicted, wasps produced more sons than daughters in all treatments. Males reared at high density (a potential cue for superparasitism) produced a higher male bias in their offspring than low-density males. Unexpectedly, female density did not affect offspring sex ratios. We discuss possible mechanisms for paternal control over offspring sex.